Distributed privacy preserving technology in dynamic networks

With the development of information technology, large-scale social network graph data have been produced and released to provide data analysis for scientific research and business structures, while traditional network privacy protection technology does not meet the actual requirements. In this paper, we address the privacy risks of link disclosure in sequential release of a dynamic network. To prevent privacy breaches, we proposed the privacy model km - number of mutual friend, where k indicates the privacy level and m is a time period that an adversary can monitor a victim to collect the attack knowledge. We present a distributed algorithm to generate releases by adding nodes in parallel. Further, in order to improve availability of anonymous graphs, distributed greedy merge noise node algorithm (DGMNNA) is designed to reduce the number of nodes added under the premise of satisfying the anonymous model. The experimental results show that the proposed algorithm can efficiently handle large-scale social network data while ensuring the availability of anonymous data.